Dr Alex Young ⚡️ Profile picture
May 24, 2025 14 tweets 5 min read Read on X
ChatGPT is a superpower.

But most people don't know how to unlock its full potential.

Here are 10 ChatGPT prompts so powerful and useful, they feel illegal to use: Image
1. Learn anything from a 20-year expert even if you're clueless

"Pretend you are an expert with 20 years of experience in {industry/topic}. Break down the core principles a total beginner must understand. Use analogies, step-by-step logic, and simplify everything like I’m 5."
2. Brutally honest thought partner to sharpen your thinking

"Act as my personal thought partner. I’ll describe {my idea/problem}, and I want you to question every assumption, point out blind spots, and help me evolve it into something 10x better."
3. Copywriting that actually converts

"You're a world-class copywriter. Rewrite this {landing page/sales pitch/email} to convert better. Make it punchy, concise, and persuasive. Use proven frameworks like PAS or AIDA. Here’s the original: {paste it}."
4. Psychoanalyze yourself like a Nobel psychologist would

"Act like a Nobel-winning psychologist. Analyze my behavior pattern: {describe it}. What’s likely causing it? What mindset shifts, habits, or actions would help me overcome it?"
5. Startup roadmap from idea to $10k revenue

"Be my startup mentor. I have this idea: {idea}. Help me refine it, validate the market, uncover monetization options, and outline a roadmap from MVP to first $10k in revenue."
6. Learn hard topics like you're 10 and actually remember them

"Teach me {any complex skill or topic} like I’m 10 years old. Use simple language, metaphors, and examples. After each explanation, quiz me to check my understanding and reinforce learning."
7. Turn rough notes into viral content

"You’re my ghostwriter. Turn this rough bullet outline into a high-impact {LinkedIn post / Twitter thread / Medium article}. Keep it engaging, clear, and tailored to {target audience}. Here’s my outline: {insert}" Image
8. Life stuck? Get uncomfortable truths + a real plan

"Act like my life coach. I feel stuck because {describe situation}. Ask me 5 uncomfortable questions to uncover the root issue. Then give me a brutally honest action plan to move forward." Image
9. Investor-style teardown of your startup idea

"You’re a brutally honest investor. Pitch: {insert startup idea}. Tear it apart. What’s flawed? What’s promising? What’s missing? Rate it on market, product, and founder fit. No fluff just real feedback." Image
10. A 30-day action plan to achieve any goal

"I need a personal strategy. Goal: {your goal}. Give me a 30-day plan. Break it down by week. Include specific actions, milestones, and habits. Make it realistic but challenging enough to grow." Image
Prompts like these make me feel like I’ve hired a world-class coach, strategist, and writer for free.
This might be helpful if you’re trying to get more out of AI and your workweek.

I send out a free newsletter every Sunday with tools, systems, and insights that help.

You’ll also get my Life Command Center Notion template + 1000+ ChatGPT prompts (free):

join.alexanderfyoung.com
I hope you've found this thread helpful.

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More from @AlexanderFYoung

Oct 28, 2025
🚨 The AI paper everyone’s quietly freaking out about.

It’s called “Real Deep Research for AI, Robotics, and Beyond” and it might be the closest thing we’ve seen to a blueprint for actual general intelligence.

Here’s the wild part:

Instead of memorizing patterns, this system teaches AI to form internal hypotheses test them, refine them, reuse them across everything from reasoning benchmarks to robotic control.

The results are absurd:

→ Beats GPT-4 and Gemini 2.5 on 40+ reasoning tasks
→ 3× faster at real-world robotics loops
→ Learns across domains without fine-tuning

This isn’t another “bigger model = smarter AI” story.

It’s the first sign of machines that do their own research.

If this scales, “understanding” won’t just be something AI mimics
it’ll be something it develops.Image
The Deep Research Loop:

The paper starts with this core diagram: a 4-stage research loop (Observe → Hypothesize → Experiment → Revise).

Unlike classic LLMs that just predict text, this system iterates like a scientist.

Every loop improves reasoning and robot control accuracy by up to 27%.Image
This blew my mind 🤯

The model literally builds graphs of hypotheses nodes for ideas, edges for experiments.

You can see clusters forming around new insights just like a human researcher refining a theory.

That’s not prompting that’s cognition. Image
Read 10 tweets
Oct 21, 2025
This is wild.

Perplexity just quietly killed Google Scholar.

I tested 50+ research workflows and these 10 prompts turned it into a private research assistant.

Copy & paste them now ↓ Image
1. Competitive Intelligence Deep Dive

"Analyze [company name]'s product strategy, recent feature releases, pricing changes, and customer sentiment from the last 6 months. Compare against top 3 competitors. Include any executive statements or strategy shifts."
2. Technical Paper Breakdown

"Explain this paper [paste arxiv link or title] like I'm a senior engineer. Focus on: novel contributions, implementation feasibility, benchmark comparisons, and whether claims hold up under scrutiny. Skip the background fluff."
Read 13 tweets
Oct 8, 2025
Forget “prompt engineering.”

The new skill is agent engineering.

OpenAI’s new Agent Builder lets anyone create autonomous systems with version control, safety checks, and workflow logic.

Here are 8 real agents worth building right now 👇 Image
1. The Inbox Zero Agent

This one handles all your email replies using your tone and priorities.

Connect: Gmail → Google Drive (for context)
Guardrails: “Never send emails about payments without approval.”

Prompt:

You are my email assistant. Summarize unread emails, draft replies in my tone, and flag anything urgent or personal.Image
2. The Meeting Summarizer Agent

Auto-summarizes calls and drops key takeaways in Slack.

Connect: Zoom + Slack + Dropbox
Eval: Coherence + action-item detection

Prompt:

You’re my meeting assistant. Summarize each transcript into 5 bullets: decisions, blockers, next steps, owner, and due dates.Image
Read 11 tweets
Oct 7, 2025
I went through every prompt in Anthropic’s library.

Let’s just say it makes every $300 “prompt course” online look like kindergarten.

Here’s what the pros actually do 👇
First discovery: they're obsessed with XML tags.

Not markdown. Not JSON formatting. XML.

Why? Because Claude was trained to recognize structure through tags, not just content.

Look at how Anthropic writes prompts vs how everyone else does it:

Everyone else:

You are a legal analyst. Analyze this contract and identify risks.

Anthropic's way:

Legal analyst with 15 years of M&A experience


Analyze the following contract for potential legal risks



- Focus on liability clauses
- Flag ambiguous termination language
- Note jurisdiction conflicts


The difference? Claude can parse the structure before processing content. It knows exactly what each piece of information represents.Image
Second pattern: they separate thinking from output.

Most prompts mix everything together. Anthropic isolates the reasoning process.

Standard prompt:

Analyze this data and create a report.

Anthropic's structure:


First, analyze the data following these steps:
1. Identify trends
2. Note anomalies
3. Calculate key metrics



Then create a report with:
- Executive summary (3 sentences)
- Key findings (bullet points)
- Recommendations (numbered list)


This forces Claude to think before writing. The outputs are dramatically more structured and accurate.

I tested this on 50 prompts. Accuracy jumped from 73% to 91%.Image
Read 14 tweets
Oct 2, 2025
AI agents humbled me.

I blamed the model.
I blamed OpenAI.
I blamed everything… except my prompts.

6 months later, I finally cracked it.

here’s how to build agents that actually work: Image
1. The Golden Rule of JSON Prompting:

Never assume the model knows what you want.

Bad prompt:

```
"Return a JSON with user info"
```

Good prompt:

```
Return a JSON object with exactly these fields:
{
"name": "string - full name",
"email": "string - valid email address",
"age": "number - integer between 18-100"
}
```

Specificity kills ambiguity.
2. Schema First, Always

Define your schema before writing prompts. Use this template:

```
json
{
"field_name": "type - description with constraints",
"status": "enum - one of: pending|completed|failed",
"confidence": "number - float between 0.0 and 1.0",
"metadata": "object - optional additional data"
}
```
Your agents need blueprints, not guesswork.
Read 14 tweets
Sep 25, 2025
I went from trash Claude outputs to mind-blowing results in 48 hours.

The difference? I stopped treating Claude like ChatGPT and started speaking its native language.

XML isn't just formatting. It's Claude's actual reasoning framework.

Here's the system I reverse-engineered: Image
XML tags work because Claude was trained on tons of structured data.

When you wrap instructions in <tags>, Claude treats them as separate, weighted components instead of one messy blob.

Think of it like giving Claude a filing system for your request.
Basic structure that changes everything:

XML:

You are an expert data analyst


Analyze this dataset and find the top 3 insights



This is quarterly sales data from a SaaS company



- Insight 1: [finding]
- Insight 2: [finding]
- Insight 3: [finding]


vs

General prompt:

"Analyze this data and give me insights"
Read 12 tweets

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